Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Theories (SMT) solvers to synthesise stabilising control laws and to prove their formal correctness. The ANNs are trained over a dataset of state-space samples to generate candidate control and Lyapunov functions, while the SMT solvers are tasked with certifying the correctness of the Lyapunov function over a continuous domain or by returning a counterexample. Despite the approach’s attractiveness, issues can occur due to subsequent calls of the SMT module at times returning similar counterexamples, which can turn out to be uninformative and may lead to dataset overfitting. Additionally, the control network weights are usually initialised with pre-computed gains from state-feedback controllers, e.g. Linear-Quadratic Regulators. To properly perform the initialisation requires user time and control expertise. In this work, we present an Augmented NLC method that mitigates these drawbacks, removes the need for the control initialisation and further improves counterexample generation. As a result, the proposed method allows the synthesis of nonlinear (as well as linear) control laws with the sole requirement being the knowledge of the system dynamics. The ANLC is tested over challenging benchmarks such as the Lorenz attractor and outperformed existing methods in terms of successful synthesis rate. The developed framework is released open-source at: https://github.com/grande-dev/Augmented-Neural-Lyapunov-Control .


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Augmented Neural Lyapunov Control


    Beteiligte:

    Erscheinungsdatum :

    01.01.2023


    Anmerkungen:

    IEEE Access , 11 pp. 67979-67986. (2023)


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Adaptive Control Lyapunov Functions

    Cohen, Max / Belta, Calin | Springer Verlag | 2023


    A Reinforcement Learning-Augmented Lyapunov Optimization Approach to DC Fast Charging Station Management

    Abbasi, Mohammad Hossein / Arjmandzadeh, Ziba / Mishra, Dillip Kumar et al. | IEEE | 2024


    Lyapunov control of vehicle handling dynamics

    Gordon,T.J. / Best,M.C. / Loughborough Univ.of Technology,GB | Kraftfahrwesen | 1999